from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 return [
     int(1000 * (box[0] / width)       ),
     int(1000 * (box[1] / height)       ),
     int(1000 * (box[2] / width)       ),
     int(1000 * (box[3] / height)       ),
 ]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_ = None       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      tesseract_config if tesseract_config is not None else """"""

 # apply OCR
 UpperCamelCase			  =      to_pil_image(UpperCamelCase_       )
 UpperCamelCase  ,     UpperCamelCase			  =      pil_image.size
 UpperCamelCase			  =      pytesseract.image_to_data(UpperCamelCase_							,					lang=UpperCamelCase_							,					output_type="""dict"""							,					config=UpperCamelCase_       )
 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

 # filter empty words and corresponding coordinates
 UpperCamelCase			  =      [idx for idx, word in enumerate(UpperCamelCase_       ) if not word.strip()]
 UpperCamelCase			  =      [word for idx, word in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
 UpperCamelCase			  =      [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
 UpperCamelCase			  =      [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
 UpperCamelCase			  =      [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]
 UpperCamelCase			  =      [coord for idx, coord in enumerate(UpperCamelCase_       ) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 UpperCamelCase			  =      []
 for x, y, w, h in zip(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       ):
  UpperCamelCase			  =      [x, y, x + w, y + h]
  actual_boxes.append(UpperCamelCase_       )

 # finally, normalize the bounding boxes
 UpperCamelCase			  =      []
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )       )

 assert len(UpperCamelCase_       ) == len(UpperCamelCase_       ), "Not as many words as there are bounding boxes"

 return words, normalized_boxes


class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""pixel_values"""]







   def __init__(     self						:						int   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BILINEAR   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[str] = None   ,   lowerCamelCase_						:						Optional[str] = ""   ,   **lowerCamelCase_						:						Union[str, Any]   ,   ):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      size if size is not None else {"""height""": 224, """width""": 224}
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_				)

    UpperCamelCase			  =      do_resize
    UpperCamelCase			  =      size
    UpperCamelCase			  =      resample
    UpperCamelCase			  =      apply_ocr
    UpperCamelCase			  =      ocr_lang
    UpperCamelCase			  =      tesseract_config







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						PILImageResampling = PILImageResampling.BILINEAR   ,   lowerCamelCase_						:						Optional[Union[str, ChannelDimension]] = None   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCamelCase_				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""				)
    UpperCamelCase			  =      (size["""height"""], size["""width"""])
    return resize(lowerCamelCase_   ,   size=lowerCamelCase_   ,   resample=lowerCamelCase_   ,   data_format=lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						ImageInput   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Dict[str, int] = None   ,   lowerCamelCase_						:						PILImageResampling = None   ,   lowerCamelCase_						:						bool = None   ,   lowerCamelCase_						:						Optional[str] = None   ,   lowerCamelCase_						:						Optional[str] = None   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   lowerCamelCase_						:						ChannelDimension = ChannelDimension.FIRST   ,   **lowerCamelCase_						:						Tuple   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      do_resize if do_resize is not None else self.do_resize
    UpperCamelCase			  =      size if size is not None else self.size
    UpperCamelCase			  =      get_size_dict(lowerCamelCase_				)
    UpperCamelCase			  =      resample if resample is not None else self.resample
    UpperCamelCase			  =      apply_ocr if apply_ocr is not None else self.apply_ocr
    UpperCamelCase			  =      ocr_lang if ocr_lang is not None else self.ocr_lang
    UpperCamelCase			  =      tesseract_config if tesseract_config is not None else self.tesseract_config

    UpperCamelCase			  =      make_list_of_images(lowerCamelCase_				)

    if not valid_images(lowerCamelCase_				):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""				)

    if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""				)

    # All transformations expect numpy arrays.
    UpperCamelCase			  =      [to_numpy_array(lowerCamelCase_				) for image in images]

    if apply_ocr:
     requires_backends(self   ,   """pytesseract"""				)
     UpperCamelCase			  =      []
     UpperCamelCase			  =      []
     for image in images:
      UpperCamelCase  ,     UpperCamelCase			  =      apply_tesseract(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)
      words_batch.append(lowerCamelCase_				)
      boxes_batch.append(lowerCamelCase_				)

    if do_resize:
     UpperCamelCase			  =      [self.resize(image=lowerCamelCase_   ,   size=lowerCamelCase_   ,   resample=lowerCamelCase_				) for image in images]

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    UpperCamelCase			  =      [flip_channel_order(lowerCamelCase_				) for image in images]
    UpperCamelCase			  =      [to_channel_dimension_format(lowerCamelCase_   ,   lowerCamelCase_				) for image in images]

    UpperCamelCase			  =      BatchFeature(data={"""pixel_values""": images}   ,   tensor_type=lowerCamelCase_				)

    if apply_ocr:
     UpperCamelCase			  =      words_batch
     UpperCamelCase			  =      boxes_batch
    return data

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
